基于主成分模糊分析的多传感器数据融合特征降维

H. Kashanian, E. Dabaghi
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引用次数: 4

摘要

如今,在许多不同的应用中,使用不同的工具,最重要的研究领域都集中在如何获得意识上。其中一个重要的应用是对患者行为和活动的认识。其重要性是由于个人需要无处不在的医疗保健。医生了解病人的身体状况,有时是很重要的。当然,这些信息还有其他重要的应用。测量、收集和分析身体行为和活动信息的方法和工具多种多样。实现这一目标最成功的工具之一是无处不在的智能电子设备,特别是智能手机和智能手表。这些设备中有许多传感器,其中一些可以用来了解日常生活的活动。作为输出结果,这些传感器产生许多原始数据。因此,需要对这些信息进行处理,并识别这种处理的输出的个体行为。本文提出了该工艺分析阶段的基本组成部分。仿真结果验证了该方法的优越性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Feature Dimension Reduction of Multisensor Data Fusion using Principal Component Fuzzy Analysis
These days, the most important areas of research in many different applications, with different tools, are focused on how to get awareness. One of the serious applications is the awareness of the behavior and activities of patients. The importance is due to the need of ubiquitous medical care for individuals. That the doctor knows the patient's physical condition, sometimes is very important. Of course, there are other important applications for this information. There are a variety of methods and tools for measurement, gathering, and analysis of the physical behaviors and activities’ information. One of the most successful tools for this aim are ubiquitous intelligent electronic devices, specifically smartphones, and smart watches. There are many sensors in these devices, some of which can be used to understand the activities of daily living. As an output result, these sensors produce many raw data. Thus, it is needed to process these information and recognize the individual behavior of the output of this processing. In this paper, the basic components of the analysis phase for this process have been proposed. Simulations validate the benefits and superiority of this method.
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